Volume-12 , Issue-4 , Apr -2024, E-ISSN: 2347-2693

Predictive maintenance is crucial for minimizing unplanned downtime in industrial machinery. This research proposes a hybrid deep learning approach using Convolutional LSTM Networks (Conv-LSTM) for fault detection in wind turbine gearboxes. The Conv-LSTM model combines convolutional neural networks (CNNs) for spatial feature extraction and long short-term memory (LSTM) networks for temporal modeling, enabling it to capture intricate patterns in multivariate sensor data. The approach was evaluated on the AI4I Predictive Maintenance dataset from Kaggle, containing real-world sensor readings from an operational wind turbine gearbox. The Conv-LSTM architecture processes raw sensor data through convolutional and LSTM layers trained jointly to learn hierarchical representations of the gearbox dynamics. Extensive experiments demonstrated the model`s outstanding performance, achieving an impressive 97.9% accuracy in classifying whether a fault condition exists in the gearbox and a corresponding loss of 0.0059 after ten epochs of training. This high predictive accuracy allows wind farm operators to anticipate potential gearbox failures proactively, enabling timely maintenance and minimizing costly downtime. The proposed approach contributes to the efficiency and sustainability of wind energy operations.

Predictive Maintenance, Convolutional Neural Network, Long Short-Term Memory, Engine Failure, Industrial Machinery, Sensor Data

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M.T. Stow, "Hybrid Deep Learning Approach for Predictive Maintenance of Industrial Machinery using Convolutional LSTM Networks," International Journal of Computer Sciences and Engineering, Vol.12, Issue.4, pp.1-11, 2024.

In the ever-evolving landscape of Cybersecurity, the detection and mitigation of network intrusions and anomalous activities remain formidable challenges. Conventional methods for identifying threats often encounter difficulties in scaling up and adapting swiftly, as they heavily rely on labeled network data. Furthermore, a narrow focus on individual data points may inadvertently overlook critical details at the packet level, thus exposing vulnerabilities that malicious actors can exploit. To confront these ongoing challenges head-on, Graph Neural Networks (GNNs) emerge as a promising solution. Their innate ability to comprehend complex network structures equips them with the capability to provide deeper insights into the dynamics of network traffic. By harnessing the power of GNN, it autonomously detects and comprehends intrusions and anomalies, surpassing the limitations of conventional techniques. Through experimentation and evaluation on real-world datasets, the proposed system demonstrates promising results in accurately identifying and classifying network intrusions.

Cyber Security, Network intrusions, Graph Neural Networks (GNN), Packet level analysis, Performance metrics, Effectiveness Evaluation, Bot-Iot.

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Bhavya Lahari Vaddempudi, Amrutha Tulabandu, S.N.B. Tanuja Reddy, Deepika Leela Pudi, Venkata Narayana Yerininti, "An Enhanced Intrusion Detection System Using Edge Centric Approach," International Journal of Computer Sciences and Engineering, Vol.12, Issue.4, pp.12-16, 2024.